Hayri Volkan Agun created SPARK-16098:
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Summary: Multiclass SVM Learning
Key: SPARK-16098
URL: https://issues.apache.org/jira/browse/SPARK-16098
Project: Spark
Issue Type: Request
Components: ML, MLlib
Affects Versions: 2.0.0, 2.1.0
Environment: Spark MLLib and ML 1.6.1
Reporter: Hayri Volkan Agun
Fix For: 2.1.0
There exists a OneVsRest classifier for using all binary classification
classifiers in multi-class classification. However for Linear SVM using
OneVsRest may create an imbalanced dataset scenarios where SVM of Spark
certainly fails. I verified this by creating LinearSVM classifier and
implemented predictRaw method of ClassificationModel class. In all experiments
the results came very poor in terms of F-Measure. The only explanation is SVM
is very sensitive to imbalanced dataset, and naturally OneVsRest classifier
creates an imbalanced dataset.
For multi-class classification, linear SVM can be optimized by considering
imbalanced datasets.
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